{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1fgVWTMK9SNz"
      },
      "source": [
        "~~~\n",
        "Copyright 2025 Google LLC\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "you may not use this file except in compliance with the License.\n",
        "You may obtain a copy of the License at\n",
        "\n",
        "    https://www.apache.org/licenses/LICENSE-2.0\n",
        "\n",
        "Unless required by applicable law or agreed to in writing, software\n",
        "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "See the License for the specific language governing permissions and\n",
        "limitations under the License.\n",
        "~~~\n",
        "\n",
        "# Agentic-Tx Demo with Hugging Face\n",
        "\n",
        "<table><tbody><tr>\n",
        "  <td style=\"text-align: center\">\n",
        "    <a href=\"https://colab.research.google.com/github/google-gemini/gemma-cookbook/blob/main/TxGemma/[TxGemma]Agentic_Demo_with_Hugging_Face.ipynb\">\n",
        "      <img alt=\"Google Colab logo\" src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" width=\"32px\"><br> Run in Google Colab\n",
        "    </a>\n",
        "  </td>\n",
        "  <td style=\"text-align: center\">\n",
        "    <a href=\"https://github.com/google-gemini/gemma-cookbook/blob/main/TxGemma/%5BTxGemma%5DAgentic_Demo_with_Hugging_Face.ipynb\">\n",
        "      <img alt=\"GitHub logo\" src=\"https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png\" width=\"32px\"><br> View on GitHub\n",
        "    </a>\n",
        "  </td>\n",
        "  <td style=\"text-align: center\">\n",
        "    <a href=\"https://huggingface.co/collections/google/txgemma-release-67dd92e931c857d15e4d1e87\">\n",
        "      <img alt=\"Hugging Face logo\" src=\"https://huggingface.co/front/assets/huggingface_logo-noborder.svg\" width=\"32px\"><br> View on Hugging Face\n",
        "    </a>\n",
        "  </td>\n",
        "</tr></tbody></table>\n",
        "\n",
        "This Colab notebook provides a basic demo of using Agentic-Tx, a therapeutics-focused LLM agent. Agentic-Tx builds on TxGemma, a collection of large language models built upon Gemma 2, that generates predictions, classifications or text based on therapeutic related data.\n",
        "\n",
        "Learn more about TxGemma at [this page](https://developers.google.com/health-ai-developer-foundations/txgemma)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t9xt2XZgaaH2"
      },
      "source": [
        "## Setup\n",
        "\n",
        "To complete this tutorial, you'll need to have a Colab runtime with sufficient resources to run the TxGemma model. Choose an appropriate runtime when starting your Colab session.\n",
        "\n",
        "You can try out TxGemma 2B and 9B* for free using a T4 GPU:\n",
        "\n",
        "1. In the upper-right of the Colab window, select **▾ (Additional connection options)**.\n",
        "2. Select **Change runtime type**.\n",
        "3. Under **Hardware accelerator**, select **T4 GPU**.\n",
        "\n",
        "*To run the demo with both TxGemma 2B predict and 9B chat models on a T4 GPU, use 4-bit quantization to reduce memory usage and speed up inference. Note that the performance of quantized versions has not been evaluated."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L9ITcQtdal7J"
      },
      "source": [
        "### Get access to TxGemma\n",
        "\n",
        "Before you get started, make sure that you have access to TxGemma models on Hugging Face:\n",
        "\n",
        "1. If you don't already have a Hugging Face account, you can create one for free by clicking [here](https://huggingface.co/join).\n",
        "2. Head over to the [TxGemma model page](https://huggingface.co/google/txgemma-2b-predict) and accept the usage conditions."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qRFQnPL2a9Dj"
      },
      "source": [
        "### Configure your HF token and Gemini token\n",
        "\n",
        "Add your Hugging Face & Gemini token to the Colab Secrets manager to securely store it.\n",
        "\n",
        "1. Ensure you have a [Gemini API key](https://ai.google.dev/gemini-api/docs/api-key) and a [HF_Token](https://huggingface.co/docs/hub/en/security-tokens)\n",
        "2. Open your Google Colab notebook and click on the 🔑 Secrets tab in the left panel. <img src=\"https://storage.googleapis.com/generativeai-downloads/images/secrets.jpg\" alt=\"The Secrets tab is found on the left panel.\" width=50%>\n",
        "\n",
        "3. Create two new secrets with the name `HF_TOKEN` and `GEMINI_API_KEY`.\n",
        "4. Copy/paste your token key into the Value input box of `HF_TOKEN` and `GEMINI_API_KEY` .\n",
        "5. Toggle the button on the left to allow notebook access to the secret."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SJRgvl-Wh_VM"
      },
      "outputs": [],
      "source": [
        "import os, re\n",
        "from google.colab import userdata\n",
        "import google.generativeai as genai\n",
        "# Note: `userdata.get` is a Colab API. If you're not using Colab, set the env\n",
        "# vars as appropriate for your system.\n",
        "\n",
        "os.environ[\"HF_TOKEN\"] = userdata.get(\"HF_TOKEN\")\n",
        "genai.configure(api_key=userdata.get(\"GEMINI_API_KEY\"))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qocWBSYmb0MA"
      },
      "source": [
        "### Install dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JyPXnIjML6go"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.4/44.4 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.7/9.7 MB\u001b[0m \u001b[31m77.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m345.1/345.1 kB\u001b[0m \u001b[31m40.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.1/76.1 MB\u001b[0m \u001b[31m30.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m469.0/469.0 kB\u001b[0m \u001b[31m53.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "! pip install --upgrade --quiet accelerate bitsandbytes huggingface_hub transformers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_z8YZ8pT1QD5"
      },
      "source": [
        "### Load prompt template\n",
        "\n",
        "First, load a JSON file that contains the prompt format for various TDC tasks."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tUhpMxJq1yNi"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "11d94039521046af9b281319ea5c3c6e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import json\n",
        "from huggingface_hub import hf_hub_download\n",
        "\n",
        "tdc_prompts_filepath = hf_hub_download(\n",
        "    repo_id=\"google/txgemma-2b-predict\",\n",
        "    filename=\"tdc_prompts.json\",\n",
        ")\n",
        "\n",
        "with open(tdc_prompts_filepath, \"r\") as f:\n",
        "    tdc_prompts_json = json.load(f)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4KUYpBH1cZA1"
      },
      "source": [
        "### Download the prediction and chat model from Hugging Face Hub\n",
        "\n",
        "Here, we are going on HuggingFace to download and load both the prediction and chat version of TxGemma. These will later be transformed into tools.\n",
        "\n",
        "You can select the variants to use for Agentic-Tx at the dropdown and whether you want to include a chat variant. Make sure your runtime has enough RAM to run all the models."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kTNyftcqF3YO"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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        },
        {
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        {
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          },
          "metadata": {},
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        },
        {
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          },
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        }
      ],
      "source": [
        "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
        "\n",
        "PREDICT_VARIANT = \"2b-predict\"  # @param [\"2b-predict\", \"9b-predict\", \"27b-predict\"]\n",
        "CHAT_VARIANT = \"9b-chat\" # @param [\"9b-chat\", \"27b-chat\"]\n",
        "USE_CHAT = True # @param {type: \"boolean\"}\n",
        "\n",
        "quantization_config = BitsAndBytesConfig(load_in_4bit=True)\n",
        "\n",
        "predict_tokenizer = AutoTokenizer.from_pretrained(f\"google/txgemma-{PREDICT_VARIANT}\")\n",
        "predict_model = AutoModelForCausalLM.from_pretrained(\n",
        "    f\"google/txgemma-{PREDICT_VARIANT}\",\n",
        "    device_map=\"auto\",\n",
        "    quantization_config=quantization_config,\n",
        ")\n",
        "\n",
        "if USE_CHAT:\n",
        "    chat_tokenizer = AutoTokenizer.from_pretrained(f\"google/txgemma-{CHAT_VARIANT}\")\n",
        "    chat_model = AutoModelForCausalLM.from_pretrained(\n",
        "        f\"google/txgemma-{CHAT_VARIANT}\",\n",
        "        device_map=\"auto\",\n",
        "        quantization_config=quantization_config,\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oBmbHe2Hg4Y_"
      },
      "source": [
        "### Run inference on a sample binary classification task\n",
        "\n",
        "Let's first try making predictions with both the models to make sure they are working. We're just going to use a sample TDC task with a sample drug."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UgEN-mgbO6Gm"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Instructions: Answer the following question about drug properties.\n",
            "Context: As a membrane separating circulating blood and brain extracellular fluid, the blood-brain barrier (BBB) is the protection layer that blocks most foreign drugs. Thus the ability of a drug to penetrate the barrier to deliver to the site of action forms a crucial challenge in development of drugs for central nervous system.\n",
            "Question: Given a drug SMILES string, predict whether it\n",
            "(A) does not cross the BBB (B) crosses the BBB\n",
            "Drug SMILES: CN1C(=O)CN=C(C2=CCCCC2)c2cc(Cl)ccc21\n",
            "Answer:\n",
            "\n",
            "Prediction model response: (B)\n",
            "\n",
            "Chat model response: (B)\n"
          ]
        }
      ],
      "source": [
        "# Example task and input\n",
        "task_name = \"BBB_Martins\"\n",
        "input_type = \"{Drug SMILES}\"\n",
        "drug_smiles = \"CN1C(=O)CN=C(C2=CCCCC2)c2cc(Cl)ccc21\"\n",
        "TDC_PROMPT = tdc_prompts_json[task_name].replace(input_type, drug_smiles)\n",
        "\n",
        "def txgemma_predict(prompt):\n",
        "    input_ids = predict_tokenizer(prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "    outputs = predict_model.generate(**input_ids, max_new_tokens=8)\n",
        "    return predict_tokenizer.decode(outputs[0][len(input_ids[0]):], skip_special_tokens=True)\n",
        "\n",
        "def txgemma_chat(prompt):\n",
        "    messages = [{\"role\": \"user\", \"content\": prompt}]\n",
        "    inputs = chat_tokenizer.apply_chat_template(\n",
        "        messages, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\"\n",
        "    ).to(\"cuda\")\n",
        "    outputs = chat_model.generate(input_ids=inputs, max_new_tokens=200)\n",
        "    return chat_tokenizer.decode(outputs[0, len(inputs[0]):], skip_special_tokens=True)\n",
        "\n",
        "print(TDC_PROMPT)\n",
        "print(f\"\\nPrediction model response: {txgemma_predict(TDC_PROMPT)}\")\n",
        "if USE_CHAT: print(f\"\\nChat model response: {txgemma_chat(TDC_PROMPT)}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BLh_zF2oe2QT"
      },
      "source": [
        "### Making our first TxGemma tool (Chat)\n",
        "We are now going to make a tool for our agent to use: a chat interface for our Gemini-based Agentic-Tx and TxGemma-Chat. This tool will allow Agentic-Tx to ask TxGemma therapeutically relevant questions. We're going to provide some functionality to check if the tool was used (`tool_is_used`)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nE0s6u5ge8oz"
      },
      "outputs": [],
      "source": [
        "# This will allow us to extract content from inside of ticks\n",
        "def extract_prompt(text, word):\n",
        "    code_block_pattern = rf\"```{word}(.*?)```\"\n",
        "    code_blocks = re.findall(code_block_pattern, text, re.DOTALL)\n",
        "    extracted_code = \"\\n\".join(code_blocks).strip()\n",
        "    return extracted_code\n",
        "\n",
        "# This class will allow us to inferface with TxGemma\n",
        "class TxGemmaChatTool:\n",
        "    def __init__(self):\n",
        "        self.tool_name = \"Chat Tool\"\n",
        "\n",
        "    def use_tool(self, question):\n",
        "        # Here, we are submitting a question to TxGemma\n",
        "        response = txgemma_chat(question)\n",
        "        return response\n",
        "\n",
        "    def tool_is_used(self, query):\n",
        "        # This just checks to see if the tool call was evoked\n",
        "        return \"```TxGemmaChat\" in query\n",
        "\n",
        "    def process_query(self, query):\n",
        "        # Here, we clean the query to remove the tool call\n",
        "        return extract_prompt(query, word=\"TxGemmaChat\")\n",
        "\n",
        "    def instructions(self):\n",
        "        # Here, we are **very** descriptively explaining how the tool works to the agent\n",
        "        # This will be useful later on\n",
        "        return (\n",
        "            \"=== Therapeutic Chat Tool Instructions ===\\n\"\n",
        "            \"### What This Tool Does\\n\"\n",
        "            \"The Therapeutic Chat Tool allows you to chat with a knowledgeable large language model named TxGemma trained on many therapeutics datasets.\"\n",
        "            \"### When and Why You Should Use It\\n\"\n",
        "            \"- If you have therapeutics related questions that you would benefit from asking TxGemma from.\\n\"\n",
        "            \"### How to Use It\\n\"\n",
        "            \"Format your query with triple backticks (```), and start with `TxGemmaChat`. Then on a new line:\\n\"\n",
        "            \"1) **Any question you would like to ask**\\n\\n\"\n",
        "            \"Example:\\n\"\n",
        "            \"```TxGemmaChat\\n\"\n",
        "            \"What is a common drug used to treat ovarian cancer?\\n\"\n",
        "            \"```\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7WIH0n7gfRyU"
      },
      "source": [
        "Let's now try out the tool by asking TxGemma-Chat a question."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jqiAWzFSfWgF"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Yes. \n",
            "\n"
          ]
        }
      ],
      "source": [
        "if USE_CHAT:\n",
        "    chat_tool = TxGemmaChatTool()\n",
        "    response = chat_tool.use_tool(\"Can Aspirin help with headaches? Yes or no?\")\n",
        "    print(response)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fQsHhOiAfW0-"
      },
      "source": [
        "### Making a TxGemma prediction (Clinical Toxicity)\n",
        "We are now going to make a tool for our agent that allows it to predict whether a drug will be toxic in clinical trials. For this, we will interface with the predict version of TxGemma, which has strong predictive capabilities.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dzP6UFaefXI9"
      },
      "outputs": [],
      "source": [
        "# This class will allow us to predict toxicity using TxGemma\n",
        "class ClinicalTox:\n",
        "    def __init__(self):\n",
        "        self.tool_name = \"Clinical Toxicity Prediction\"\n",
        "\n",
        "    def use_tool(self, smiles_string):\n",
        "        # Here, we are submitting the smiles to TxGemma, and returning the response\n",
        "        prediction = txgemma_predict(tdc_prompts_json[\"ClinTox\"].replace(\"{Drug SMILES}\", smiles_string))\n",
        "        if \"A\" in prediction:   response = f\"{smiles_string} is not toxic!\"\n",
        "        elif \"B\" in prediction: response = f\"{smiles_string} is toxic!\"\n",
        "        return response\n",
        "\n",
        "    def tool_is_used(self, query):\n",
        "        # This just checks to see if the tool call was evoked\n",
        "        return \"```ClinicalToxTool\" in query\n",
        "\n",
        "    def process_query(self, query):\n",
        "        # Here, we clean to query to remove the tool call\n",
        "        return extract_prompt(query, word=\"ClinicalToxTool\")\n",
        "\n",
        "    def instructions(self):\n",
        "        # Here, we are explaining how the tool works to the agent\n",
        "        return (\n",
        "            \"=== Clinical Toxicity Instructions ===\\n\"\n",
        "            \"The Clinical Toxicity Tool is designed to predict potential for toxicity for humans in clinicial trials.\\n\"\n",
        "            \"You can test the toxicity of different SMILES strings as they might affect humans.\\n\"\n",
        "            \"To properly use this tool, follow the format outlined below:\\n\"\n",
        "            \"1. **Form a clinical toxicity query**:\\n\"\n",
        "            \"```ClinicalToxTool\\n<SMILES string here>\\n```\\n\"\n",
        "            \"Example: ```ClinicalToxTool\\nCN(C)C(=N)N=C(N)N\\n```\\n\"\n",
        "            \"- Replace `<SMILES string here>` with an exact smiles string. \"\n",
        "            \"A results will be returned to you describing the clinical toxicity.\\n\"\n",
        "            \"**Important Formatting Details**:\\n\"\n",
        "            \"- Use `ClinicalToxTool` as the exact keyword to begin your query.\\n\"\n",
        "            \"- Place your text after `ClinicalToxTool` on a new line.\\n\"\n",
        "            \"- Enclose the entire query using three backticks (```), as shown in the example above.\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pcxJSeOdiwb_"
      },
      "source": [
        "Now we can test this out. The drug `CC(=O)OC1=CC=CC=C1C(=O)O` is a well known non-toxic drug, Aspirin. Let's test out the ClinicalTox to make sure the tool works correctly!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "trTNftLCix7L"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CC(=O)OC1=CC=CC=C1C(=O)O is not toxic!\n"
          ]
        }
      ],
      "source": [
        "# This is an example of gemini using the tool to predict toxicity\n",
        "clintox = ClinicalTox()\n",
        "prediction_aspirin = clintox.use_tool(\"CC(=O)OC1=CC=CC=C1C(=O)O\")\n",
        "print(prediction_aspirin)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4qZp8hTfizAy"
      },
      "source": [
        "### Making a PubMed article search tool\n",
        "We are now going to make a tool for our agent that allows them to search through PubMed articles. This allows our agent to have access to the latest biomedical research. We can access PubMed through the `biopython` API.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qwfONVb31dUf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/3.2 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.3/3.2 MB\u001b[0m \u001b[31m7.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/3.2 MB\u001b[0m \u001b[31m31.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.2/3.2 MB\u001b[0m \u001b[31m33.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "! pip install --upgrade --quiet biopython"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bXP04E2MizVY"
      },
      "outputs": [],
      "source": [
        "from Bio import Medline, Entrez\n",
        "\n",
        "# This class will allow us to interface with PubMed\n",
        "class PubMedSearch:\n",
        "    def __init__(self):\n",
        "        self.tool_name = \"PubMed Search\"\n",
        "\n",
        "    def tool_is_used(self, query: str):\n",
        "        # This just checks to see if the tool call was evoked\n",
        "        return \"```PubMedSearch\" in query\n",
        "\n",
        "    def process_query(self, query: str):\n",
        "        # Here, we clean to query to remove the tool call\n",
        "        search_text = extract_prompt(query, word=\"PubMedSearch\")\n",
        "        return search_text.strip()\n",
        "\n",
        "    def use_tool(self, search_text):\n",
        "        # Here, we are searching through PubMed and returning relevant articles\n",
        "        pmids = list()\n",
        "        handle = Entrez.esearch(db=\"pubmed\", sort=\"relevance\", term=search_text, retmax=3)\n",
        "        record = Entrez.read(handle)\n",
        "        pmids = record.get(\"IdList\", [])\n",
        "        handle.close()\n",
        "\n",
        "        if not pmids:\n",
        "            return f\"No PubMed articles found for '{search_text}' Please try a simpler search query.\"\n",
        "\n",
        "        fetch_handle = Entrez.efetch(db=\"pubmed\", id=\",\".join(pmids), rettype=\"medline\", retmode=\"text\")\n",
        "        records = list(Medline.parse(fetch_handle))\n",
        "        fetch_handle.close()\n",
        "\n",
        "        result_str = f\"=== PubMed Search Results for: '{search_text}' ===\\n\"\n",
        "        for i, record in enumerate(records, start=1):\n",
        "            pmid = record.get(\"PMID\", \"N/A\")\n",
        "            title = record.get(\"TI\", \"No title available\")\n",
        "            abstract = record.get(\"AB\", \"No abstract available\")\n",
        "            journal = record.get(\"JT\", \"No journal info\")\n",
        "            pub_date = record.get(\"DP\", \"No date info\")\n",
        "            authors = record.get(\"AU\", [])\n",
        "            authors_str = \", \".join(authors[:3])\n",
        "            result_str += (\n",
        "                f\"\\n--- Article #{i} ---\\n\"\n",
        "                f\"PMID: {pmid}\\n\"\n",
        "                f\"Title: {title}\\n\"\n",
        "                f\"Authors: {authors_str}\\n\"\n",
        "                f\"Journal: {journal}\\n\"\n",
        "                f\"Publication Date: {pub_date}\\n\"\n",
        "                f\"Abstract: {abstract}\\n\")\n",
        "        return f\"Query: {search_text}\\nResults: {result_str}\"\n",
        "\n",
        "    def instructions(self):\n",
        "        # Here, we are explaining how the tool works to the agent\n",
        "        return (\n",
        "            f\"{'@' * 10}\\n@@@ PubMed Search Tool Instructions @@@\\n\\n\"\n",
        "            \"### What This Tool Does\\n\"\n",
        "            \"The PubMed Search Tool queries the NCBI Entrez API (PubMed) for a given search phrase, \"\n",
        "            \"and retrieves metadata for a few of the top articles (PMID, title, authors, journal, date, abstract).\\n\\n\"\n",
        "            \"### When / Why You Should Use It\\n\"\n",
        "            \"- To find **scientific literature** references on a specific biomedical topic.\\n\"\n",
        "            \"- To retrieve **abstracts, titles, authors**, and other metadata.\\n\\n\"\n",
        "            \"### Query Format\\n\"\n",
        "            \"Wrap your request with triple backticks, starting with `PubMedSearch`. For example:\\n\\n\"\n",
        "            \"```PubMedSearch\\ncancer immunotherapy\\n```\\n\\n\"\n",
        "            \"### Example\\n\"\n",
        "            \"```PubMedSearch\\nmachine learning in drug discovery\\n```\\n\"\n",
        "            \"- This will search PubMed for articles related to 'machine learning in drug discovery', \"\n",
        "            \"fetch up to 3 PMIDs, and return their titles, abstracts, etc.\\n\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wl23xROHkRI_"
      },
      "source": [
        "Let's now see how this tool works by getting the most up-to-date knowledge on drugs used for ovarian cancer."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OMWEiM29kQ4o"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/Bio/Entrez/__init__.py:734: UserWarning: \n",
            "            Email address is not specified.\n",
            "\n",
            "            To make use of NCBI's E-utilities, NCBI requires you to specify your\n",
            "            email address with each request.  As an example, if your email address\n",
            "            is A.N.Other@example.com, you can specify it as follows:\n",
            "               from Bio import Entrez\n",
            "               Entrez.email = 'A.N.Other@example.com'\n",
            "            In case of excessive usage of the E-utilities, NCBI will attempt to contact\n",
            "            a user at the email address provided before blocking access to the\n",
            "            E-utilities.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Query: Drugs used for ovarian cancer\n",
            "Results: === PubMed Search Results for: 'Drugs used for ovarian cancer' ===\n",
            "\n",
            "--- Article #1 ---\n",
            "PMID: 33123161\n",
            "Title: Immunotherapy for Ovarian Cancer: Adjuvant, Combination, and Neoadjuvant.\n",
            "Authors: Yang C, Xia BR, Zhang ZC\n",
            "Journal: Frontiers in immunology\n",
            "Publication Date: 2020\n",
            "Abstract: Ovarian cancer is the most lethal gynecologic malignancy. Surgery and chemotherapy are the primary treatments for ovarian cancer; however, patients often succumb to recurrence with chemotherapeutic resistance within several years after the initial treatment. In the past two decades, immunotherapy has rapidly developed, and has revolutionized the treatment of various types of cancer. Despite the fact that immunotherapy response rates among ovarian cancer patients remain modest, treatment with immune checkpoint inhibitors (ICIs), chimeric antigen receptor (CAR)- and TCR-engineered T cells is rapidly developing. Therapeutic efficiency could be improved significantly if immunotherapy is included as an adjuvant therapy, in combination with chemotherapy, radiation therapy, and the use of anti-angiogenesis drugs, and poly ADP ribose polymerase inhibitors (PARPi). Newly developed technologies that identify therapeutic targets, predict treatment efficacy, rapidly screen potential immunotherapy drugs, provide neoadjuvant immunotherapy, and utilize nanomedicine technology provide new opportunities for the treatment of ovarian cancer, and have the potential to prolong patient survival. However, important issues that may hinder the efficacy of such approaches, including hyperprogressive disease (HPD), immunotherapy-resistance, and toxicity of the treatments, including neurotoxicity, must be taken into account and addressed for these therapies to be effective.\n",
            "\n",
            "--- Article #2 ---\n",
            "PMID: 33168565\n",
            "Title: Treatment of epithelial ovarian cancer.\n",
            "Authors: Kuroki L, Guntupalli SR\n",
            "Journal: BMJ (Clinical research ed.)\n",
            "Publication Date: 2020 Nov 9\n",
            "Abstract: Ovarian cancer is the third most common gynecologic malignancy worldwide but accounts for the highest mortality rate among these cancers. A stepwise approach to assessment, diagnosis, and treatment is vital to appropriate management of this disease process. An integrated approach with gynecologic oncologists as well as medical oncologists, pathologists, and radiologists is of paramount importance to improving outcomes. Surgical cytoreduction to R0 is the mainstay of treatment, followed by adjuvant chemotherapy. Genetic testing for gene mutations that affect treatment is the standard of care for all women with epithelial ovarian cancer. Nearly all women will have a recurrence, and the treatment of recurrent ovarian cancer continues to be nuanced and requires extensive review of up to date modalities that balance efficacy with the patient's quality of life. Maintenance therapy with poly ADP-ribose polymerase inhibitors, bevacizumab, and/or drugs targeting homologous recombination deficiency is becoming more widely used in the treatment of ovarian cancer, and the advancement of immunotherapy is further revolutionizing treatment targets.\n",
            "\n",
            "--- Article #3 ---\n",
            "PMID: 31560828\n",
            "Title: Ovarian cancer: Current status and strategies for improving therapeutic outcomes.\n",
            "Authors: Chandra A, Pius C, Nabeel M\n",
            "Journal: Cancer medicine\n",
            "Publication Date: 2019 Nov\n",
            "Abstract: Of all the gynecologic tumors, ovarian cancer (OC) is known to be the deadliest. Advanced-stages of OC are linked with high morbidity and low survival rates despite the immense amount of research in the field. Shortage of promising screening tools for early-stage detection is one of the major challenges linked with the poor survival rate for patients with OC. In OC, therapeutic management is used with multidisciplinary approaches that includes debulking surgery, chemotherapy, and (rarely) radiotherapy. Recently, there is an increasing interest in using immunomodulation for treating OC. Relapse rates are high in this malignancy and averages around every 2-years. Further treatments after the relapse are more intense, increasing the toxicity, resistance to chemotherapy drugs, and financial burden to patients with poor quality-of-life. A procedure that has been studied to help reduce the morbidity rate involves pre-sensitizing cancer cells with standard therapy in order to produce optimal results with minimum dosage. Utilizing such an approach, platinum-based agents are effective due to their increased response to platinum-based chemotherapy in relapsed cases. These chemo-drugs also help address the issue of drug resistance. After conducting an extensive search with available literature and the resources for clinical trials, information is precisely documented on current research, biomarkers, options for treatment and clinical trials. Several schemes for enhancing the therapeutic responses for OC are discussed systematically in this review with an attempt in summarizing the recent developments in this exciting field of translational/clinical research.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "pubmed_tool = PubMedSearch()\n",
        "search_results = pubmed_tool.use_tool(\"Drugs used for ovarian cancer\")\n",
        "print(search_results)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GXejx5atkSdX"
      },
      "source": [
        "## Wrapping it all together"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rbW1B_bCz98Q"
      },
      "source": [
        "### Creating a tool manager\n",
        "Now we need to package all of this together in a way that allows our agent to use these tools."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2UTRDs7wkS4V"
      },
      "outputs": [],
      "source": [
        "# The tool manager will hold all of the tools, and provide an interface for the agent\n",
        "class ToolManager:\n",
        "    def __init__(self, toolset):\n",
        "        self.toolset = toolset\n",
        "\n",
        "    def tool_prompt(self):\n",
        "        # This will let the agent know what tools it has access to\n",
        "        tool_names = \", \".join([tool.tool_name for tool in self.toolset])\n",
        "        return f\"You have access to the following tools: {tool_names}\\n{self.tool_instructions()}. You can only use one tool at a time. These are the only tools you have access to nothing else.\"\n",
        "\n",
        "    def tool_instructions(self):\n",
        "        # This allows the agent to know how to use the tools\n",
        "        tool_instr = \"\\n\".join([tool.instructions() for tool in self.toolset])\n",
        "        return f\"The following is a set of instructions on how to use each tool.\\n{tool_instr}\"\n",
        "\n",
        "    def use_tool(self, query):\n",
        "        # This will iterate through all of the tools\n",
        "        # and find the correct tool that the agent requested\n",
        "        for tool in self.toolset:\n",
        "            if tool.tool_is_used(query):\n",
        "                # use the tool and return the output\n",
        "                return tool.use_tool(tool.process_query(query))\n",
        "        return f\"No tool match for search: {query}\"\n",
        "\n",
        "if USE_CHAT:\n",
        "    tools = ToolManager([TxGemmaChatTool(), ClinicalTox(), PubMedSearch()])\n",
        "else:\n",
        "    tools = ToolManager([ClinicalTox(), PubMedSearch()])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wgIxiRppsxL3"
      },
      "source": [
        "### Creating a Gemini inference tool\n",
        "The following tool will allow us to inference Gemini, which we can use to act as the agent orchestrator. Gemini will pick from the different tools we implemented as well as provide input to those tools in order to solve a problem. Let's make the inference structure."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j6LXiWUPsxiD"
      },
      "outputs": [],
      "source": [
        "def inference_gemini(prompt, system_prompt, model_str):\n",
        "    # Check to see that our model string matches\n",
        "    if model_str == \"gemini-2.5-pro\":\n",
        "        model = genai.GenerativeModel(model_name=\"gemini-2.5-pro\", system_instruction=system_prompt)\n",
        "        response = model.generate_content(prompt)\n",
        "        answer = response.text\n",
        "    return answer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q_ZltV6-CZB-"
      },
      "source": [
        "## Creating a therapeutics agent\n",
        "\n",
        "Finally, we are going to create a Agentic-Tx with access to the three tools we made here, a therapeutics agent. We are going to ask it which drug is preferable for further development: a preferable drug, `CC(=O)OC1=CC=CC=C1C(=O)O`, versus a non-preferable drug, `O=C(CCCCCCC(=O)Nc1ccccc1)NO`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WSvtrelYCZYh"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "[Thought 1]\n",
            "\n",
            " To determine which drug is preferred, I will first identify each compound from its SMILES string and then use computational tools to analyze their key drug development properties, such as physicochemical characteristics (Lipinski's Rule of Five), ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) profiles, and known biological activity. A comprehensive comparison of these factors will allow me to make an evidence-based recommendation for further development.\n",
            "\n",
            "[Action 1]\n",
            "\n",
            " I will start by analyzing the clinical toxicity of the first drug, Aspirin. This will provide an initial assessment of its safety profile, which is a critical factor in drug development. A lower toxicity prediction would favor its selection.```ClinicalToxTool\n",
            "CC(=O)OC1=CC=CC=C1C(=O)O\n",
            "```\n",
            "\n",
            "[Observation 1]\n",
            "\n",
            " CC(=O)OC1=CC=CC=C1C(=O)O is not toxic!\n",
            "\n",
            "[Thought 2]\n",
            "\n",
            " To make an informed decision, I will first identify the second compound and then perform a comparative analysis of both drugs' essential properties. This will include assessing their ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) profiles and evaluating their drug-likeness based on physicochemical characteristics like Lipinski's Rule of Five.\n",
            "\n",
            "[Action 2]\n",
            "\n",
            " I will now proceed by assessing the clinical toxicity of the second drug candidate, suberanilinohydroxamic acid (SAHA), also known as Vorinostat. This will allow for a direct comparison of the safety profiles of both compounds, which is a crucial step in determining which is more favorable for further development.\n",
            "```ClinicalToxTool\n",
            "O=C(CCCCCCC(=O)Nc1ccccc1)NO\n",
            "```\n",
            "\n",
            "[Observation 2]\n",
            "\n",
            " O=C(CCCCCCC(=O)Nc1ccccc1)NO is toxic!\n",
            "\n",
            "[Thought 3]\n",
            "\n",
            " To make a definitive recommendation, I will first use a tool to identify the known biological targets and therapeutic indications of both drugs, as this will contextualize the toxicity findings. Following that, I will use another tool to evaluate their full ADMET profiles and key physicochemical properties to comprehensively compare their drug-likeness and development potential.\n",
            "\n",
            "[Action 3]\n",
            "\n",
            " To provide a comprehensive recommendation, it's essential to understand the therapeutic context of each compound. The clinical toxicity results are a critical piece of the puzzle, but a drug's efficacy and intended use are equally important. I will now use the PubMed Search tool to gather information on the known biological activities and therapeutic applications of both drugs.\n",
            "\n",
            "First, I will research the first compound, acetylsalicylic acid (Aspirin).```PubMedSearch\n",
            "acetylsalicylic acid therapeutic use\n",
            "```\n",
            "\n",
            "[Observation 3]\n",
            "\n",
            " Query: acetylsalicylic acid therapeutic use\n",
            "Results: === PubMed Search Results for: 'acetylsalicylic acid therapeutic use' ===\n",
            "\n",
            "--- Article #1 ---\n",
            "PMID: 28106908\n",
            "Title: The aspirin story - from willow to wonder drug.\n",
            "Authors: Desborough MJR, Keeling DM\n",
            "Journal: British journal of haematology\n",
            "Publication Date: 2017 Jun\n",
            "Abstract: The story of the discovery of aspirin stretches back more than 3500 years to when bark from the willow tree was used as a pain reliever and antipyretic. It involves an Oxfordshire clergyman, scientists at a German dye manufacturer, a Nobel Prize-winning discovery and a series of pivotal clinical trials. Aspirin is now the most commonly used drug in the world. Its role in preventing cardiovascular and cerebrovascular disease has been revolutionary and one of the biggest pharmaceutical success stories of the last century.\n",
            "\n",
            "--- Article #2 ---\n",
            "PMID: 31669590\n",
            "Title: Aspirin and its pleiotropic application.\n",
            "Authors: Hybiak J, Broniarek I, Kiryczynski G\n",
            "Journal: European journal of pharmacology\n",
            "Publication Date: 2020 Jan 5\n",
            "Abstract: Aspirin (acetylsalicylic acid), the oldest synthetic drug, was originally used as an anti-inflammatory medication. Being an irreversible inhibitor of COX (prostaglandin-endoperoxide synthase) enzymes that produce precursors for prostaglandins and thromboxanes, it has gradually found several other applications. Sometimes these applications are unrelated to its original purpose for example its use as an anticoagulant. Applications such as these have opened opportunities for new treatments. In this case, it has been tested in patients with cardiovascular disease to reduce the risk of myocardial infarct. Its function as an anticoagulant has also been explored in the prophylaxis and treatment of pre-eclampsia, where due to its anti-inflammatory properties, aspirin intake may be used to reduce the risk of colorectal cancer. It is important to always consider both the risks and benefits of aspirin's application. This is especially important for proposed use in the prevention and treatment of neurologic ailments like Alzheimer's disease, or in the prophylaxis of myocardial infarct. In such cases, the decision if aspirin should be applied, and at what dose may be guided by specific molecular markers. In this revived paper, the pleiotropic application of aspirin is summarized.\n",
            "\n",
            "--- Article #3 ---\n",
            "PMID: 30391545\n",
            "Title: The first 3500 years of aspirin history from its roots - A concise summary.\n",
            "Authors: Montinari MR, Minelli S, De Caterina R\n",
            "Journal: Vascular pharmacology\n",
            "Publication Date: 2019 Feb\n",
            "Abstract: Aspirin is currently the most widely used drug worldwide, and has been clearly one of the most important pharmacological achievements of the twentieth century. Historians of medicine have traced its birth in 1897, but the fascinating history of aspirin actually dates back >3500 years, when willow bark was used as a painkiller and antipyretic by Sumerians and Egyptians, and then by great physicians from ancient Greece and Rome. The modern history of aspirin precursors, salicylates, began in 1763 with Reverend Stone - who first described their antipyretic effects - and continued in the 19th century with many researchers involved in their extraction and chemical synthesis. Bayer chemist Felix Hoffmann synthesized aspirin in 1897, and 70 years later the pharmacologist John Vane elucidated its mechanism of action in inhibiting prostaglandin production. Originally used as an antipyretic and anti-inflammatory drug, aspirin then became, for its antiplatelet properties, a milestone in preventing cardiovascular and cerebrovascular diseases. The aspirin story continues today with the growing evidence of its chemopreventive effect against colorectal and other types of cancer, now awaiting the results of ongoing primary prevention trials in this setting. This concise review revisits the history of aspirin with a focus on its most remote origins.\n",
            "\n",
            "\n",
            "[Thought 4]\n",
            "\n",
            " To complete the comparison, I will research the therapeutic use of the second drug, suberanilinohydroxamic acid (SAHA/Vorinostat), to understand its clinical applications. This will allow for a balanced assessment against Aspirin, considering both the toxicity data and the established therapeutic value of each compound.\n",
            "\n",
            "[Action 4]\n",
            "\n",
            " ```PubMedSearch\n",
            "suberanilinohydroxamic acid therapeutic use\n",
            "```\n",
            "\n",
            "[Observation 4]\n",
            "\n",
            " Query: suberanilinohydroxamic acid therapeutic use\n",
            "Results: === PubMed Search Results for: 'suberanilinohydroxamic acid therapeutic use' ===\n",
            "\n",
            "--- Article #1 ---\n",
            "PMID: 33477371\n",
            "Title: Pathological Mechanisms of Bortezomib-Induced Peripheral Neuropathy.\n",
            "Authors: Yamamoto S, Egashira N\n",
            "Journal: International journal of molecular sciences\n",
            "Publication Date: 2021 Jan 17\n",
            "Abstract: Bortezomib, a first-generation proteasome inhibitor widely used in chemotherapy for hematologic malignancy, has effective anti-cancer activity but often causes severe peripheral neuropathy. Although bortezomib-induced peripheral neuropathy (BIPN) is a dose-limiting toxicity, there are no recommended therapeutics for its prevention or treatment. One of the most critical problems is a lack of knowledge about pathological mechanisms of BIPN. Here, we summarize the known mechanisms of BIPN based on preclinical evidence, including morphological abnormalities, involvement of non-neuronal cells, oxidative stress, and alterations of transcriptional programs in both the peripheral and central nervous systems. Moreover, we describe the necessity of advancing studies that identify the potential efficacy of approved drugs on the basis of pathological mechanisms, as this is a convincing strategy for rapid translation to patients with cancer and BIPN.\n",
            "\n",
            "--- Article #2 ---\n",
            "PMID: 27931952\n",
            "Title: Chemical Peeling: A Useful Tool in the Office.\n",
            "Authors: Truchuelo M, Cerda P, Fernandez LF\n",
            "Journal: Actas dermo-sifiliograficas\n",
            "Publication Date: 2017 May\n",
            "Abstract: Chemical peeling is a common treatment in cosmetic dermatology. A peel that has been used for many years is trichloroacetic acid. Its adverse effects have for a long time been a major limitation. We present a practical review of the characteristics, mechanisms of action, indications, and complications of superficial chemical peels and of peeling with trichloroacetic acid.\n",
            "\n",
            "--- Article #3 ---\n",
            "PMID: 12908468\n",
            "Title: Bortezomib.\n",
            "Authors: Paramore A, Frantz S\n",
            "Journal: Nature reviews. Drug discovery\n",
            "Publication Date: 2003 Aug\n",
            "Abstract: No abstract available\n",
            "\n",
            "\n",
            "[Final Response]\n",
            "\n",
            " Based on the comprehensive analysis conducted, the drug preferred for further development is **1. CC(=O)OC1=CC=CC=C1C(=O)O (Aspirin)**.\n",
            "\n",
            "Here is a summary of the findings that support this conclusion:\n",
            "\n",
            "1.  **Compound Identification**:\n",
            "    *   Drug 1 is **Acetylsalicylic Acid (Aspirin)**, a very common nonsteroidal anti-inflammatory drug (NSAID).\n",
            "    *   Drug 2 is **Suberanilinohydroxamic Acid (SAHA, Vorinostat)**, a histone deacetylase (HDAC) inhibitor used as an anti-cancer agent.\n",
            "\n",
            "2.  **Clinical Toxicity**:\n",
            "    *   The analysis predicted that **Aspirin is not toxic**. This aligns with its widespread use as an over-the-counter medication for pain, fever, and inflammation, as well as for long-term, low-dose therapy in cardiovascular disease prevention.\n",
            "    *   Conversely, the analysis predicted that **Vorinostat is toxic**. This is consistent with its clinical use as a chemotherapy drug, where significant side effects are often accepted due to the severity of the disease (e.g., cutaneous T-cell lymphoma). Toxicity is a major limiting factor in its use and development.\n",
            "\n",
            "3.  **Therapeutic Application**:\n",
            "    *   **Aspirin** is described in the literature as a \"wonder drug\" with a history of safe and effective use spanning millennia for pain and fever, and decades for cardiovascular protection. It has a broad, established, and revolutionary role in medicine.\n",
            "    *   **Vorinostat** is a specialized drug for oncology. While effective for certain cancers, its application is narrow and its development is hampered by dose-limiting toxicities, a common challenge for chemotherapeutic agents.\n",
            "\n",
            "**Conclusion:**\n",
            "\n",
            "For a drug to be considered for broad development, a favorable safety and toxicity profile is paramount. **Aspirin (Drug 1)** has a demonstrably superior safety profile compared to Vorinostat (Drug 2). Its wide range of proven therapeutic benefits and lower toxicity make it the unequivocally preferred candidate for further development over a compound with predicted toxicity and a more niche, high-risk therapeutic application.\n"
          ]
        }
      ],
      "source": [
        "# This class defines our Agentic-Tx, wrapping together all of our tools and the orchestrator\n",
        "class AgenticTx:\n",
        "    def __init__(self, tool_manager, model_str, num_steps=5):\n",
        "        self.curr_steps = 0\n",
        "        self.num_steps = num_steps\n",
        "        self.model_str = model_str\n",
        "        self.tool_manager = tool_manager\n",
        "        self.thoughts = list()\n",
        "        self.actions  = list()\n",
        "        self.observations = list()\n",
        "\n",
        "    def reset(self):\n",
        "        # Reset the number of steps taken\n",
        "        self.curr_steps = 0\n",
        "\n",
        "    def system_prompt(self, use_tools=True):\n",
        "        # These are the system instructions for AgenticTx\n",
        "        role_prompt = \"You are an expert therapeutic agent. You answer accurately and thoroughly.\"\n",
        "        prev_actions = f\"You can perform a maximum of {self.num_steps} actions. You have performed {self.curr_steps} and have {self.num_steps - self.curr_steps - 1} left.\"\n",
        "        if use_tools: tool_prompt = \"You can use tools to solve problems and answer questions. \" + self.tool_manager.tool_prompt()\n",
        "        else: tool_prompt = \"You cannot use any tools right now.\"\n",
        "        return f\"{role_prompt} {prev_actions} {tool_prompt}\"\n",
        "\n",
        "    def prior_information(self, query):\n",
        "        info_txt = f\"Question: {query}\\n\" if query is not None else \"\"\n",
        "        for i in range(self.curr_steps):\n",
        "            info_txt += f\"### Thought {i + 1}: {self.thoughts[i]}\\n\"\n",
        "            info_txt += f\"### Action {i + 1}: {self.actions[i]}\\n\"\n",
        "            info_txt += f\"### Observation {i + 1}: {self.observations[i]}\\n\\n\"\n",
        "            info_txt += \"@\"*20\n",
        "        return info_txt\n",
        "\n",
        "    def step(self, question):\n",
        "        for i in range(self.num_steps):\n",
        "            if self.curr_steps == self.num_steps-1:\n",
        "                return inference_gemini(\n",
        "                    model_str=self.model_str,\n",
        "                    prompt=f\"{self.prior_information(question)}\\nYou must now provide an answer to this question {question}\",\n",
        "                    system_prompt=self.system_prompt(use_tools=False))\n",
        "            else:\n",
        "                # Provide a thought step, planning for the model\n",
        "                thought = inference_gemini(\n",
        "                    model_str=self.model_str,\n",
        "                    prompt=f\"{self.prior_information(question)}\\nYou cannot currently use tools but you can think about the problem and what tools you want to use. This was the question, think about plans for how to use tools to answer this {question}. Let's think step by step (respond with only 1-2 sentences).\\nThought: \",\n",
        "                    system_prompt=self.system_prompt(use_tools=False))\n",
        "                # Provide a took action for the model\n",
        "                action = inference_gemini(\n",
        "                    model_str=self.model_str,\n",
        "                    prompt=f\"{self.prior_information(question)}\\n{thought}\\nNow you must use tools to answer the following user query [{question}], closely following the tool instructions. Tool\",\n",
        "                    system_prompt=self.system_prompt(use_tools=True))\n",
        "                obs = self.tool_manager.use_tool(action)\n",
        "\n",
        "                print(f\"\\n[Thought {i + 1}]\\n\\n\", thought)\n",
        "                print(f\"\\n[Action {i + 1}]\\n\\n\", action)\n",
        "                print(f\"\\n[Observation {i + 1}]\\n\\n\", obs)\n",
        "\n",
        "                self.thoughts.append(thought)\n",
        "                self.actions.append(action)\n",
        "                self.observations.append(obs)\n",
        "\n",
        "                self.curr_steps += 1\n",
        "\n",
        "\n",
        "agentictx = AgenticTx(tool_manager=tools, model_str=\"gemini-2.5-pro\")\n",
        "# The model should select CC(=O)OC1=CC=CC=C1C(=O)O because O=C(CCCCCCC(=O)Nc1ccccc1)NO is toxic\n",
        "response = agentictx.step(\"Which of the following drugs is preferred for further development? 1. CC(=O)OC1=CC=CC=C1C(=O)O or 2. O=C(CCCCCCC(=O)Nc1ccccc1)NO\")\n",
        "print(\"\\n[Final Response]\\n\\n\", response)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "[TxGemma]Agentic_Demo_with_Hugging_Face.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
